Orchestrated Experiment Lifecycle Management

• Orchestrated experiment lifecycle management is a systematic framework that defines, executes, and monitors every phase of an experiment from design to archival.
• It employs formal models like DAGs and reusable descriptors (YAML, JSON) to streamline configuration, scheduling, and resource optimization.
• By integrating robust provenance capture, containerization, and scheduler adaptation, it guarantees transparent, repeatable, and scalable research workflows.

Orchestrated experiment lifecycle management refers to the systematic, often workflow-driven, coordination of all phases involved in a scientific experiment—from initial design and configuration through execution, monitoring, provenance capture, analysis, and archival—using software frameworks that guarantee reproducibility, transparency, and scalability. This paradigm is now central in computational, ML, physical, and domain science, enabling robust, repeatable experimentation at scale, and bridging the gap between ad hoc research code and industrial-grade workflow management (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Vargas-Solar et al., 30 Sep 2025, Fei et al., 2024).

1. Formal Models and Lifecycle Phases

Orchestrated experiment lifecycle management is underpinned by explicit workflow and data models, ensuring experimental steps are representable, executable, and introspectable. The formalization of the lifecycle varies, but consistently includes:

• Lifecycle Phases: Distinct stages such as Specification/Planning, Preparation/Configuration, Execution/Scheduling, Monitoring, Collection/Archival, and Analysis/Post-hoc aggregation (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Vargas-Solar et al., 30 Sep 2025, Fei et al., 2024, Rakotoarivelo et al., 2014).
• Workflow Representation: Experiments are encoded as directed acyclic graphs (DAGs) or similar formal constructs, with nodes representing tasks and edges capturing dependencies (Fei et al., 2024, Adamidi et al., 1 Apr 2025, 2410.1681, Wachs et al., 2016).
• Reusable Templates and Descriptors: Modular specification using YAML, JSON, XML, or Python DSLs abstract away resource details (e.g., compute clusters, containers), and parameter spaces (e.g., hyperparameter grids, input files), facilitating multi-run and gridsearch campaigns (Laszewski et al., 30 Jul 2025, Kiar et al., 2018, Arbel et al., 2024).

Typical transitions and orchestration flow can be formalized as: $S_{t+1} = f(S_t, A_t, M_t)$ where $S$ is the lifecycle state, $A$ an action, and $M$ the metadata context (Vargas-Solar et al., 30 Sep 2025). Common state machines or controller modules dispatch tasks, respect topological/task dependencies, and enforce scheduling/QoS or resource constraints (e.g., SLURM job slots, device pools) (Adamidi et al., 1 Apr 2025, Fei et al., 2024).

2. Architecture and Orchestration Mechanisms

Frameworks for orchestrated lifecycle management converge on layered or microservices-inspired architectures, decoupling user-facing control from back-end execution coordination:

• User/API Layer: CLI or web GUI for experiment submission, configuration, and monitoring (e.g., MLXP Python API, SCHEMA lab React dashboard, Cloudmesh “cms” shell) (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Laszewski et al., 30 Jul 2025).
• Workflow Engine/Execution Manager: Reads structured descriptors, creates task/workflow DAGs, resolves parameter sweeps, enforces resource quotas, and orchestrates job submission on backends (HPC schedulers, Kubernetes, local execution, cloud) (Adamidi et al., 1 Apr 2025, Kiar et al., 2018, Vargas-Solar et al., 30 Sep 2025, Fei et al., 2024).
• Resource and Device Managers: Handle device/sample locking and release (autonomous labs), or cluster job management and cloud cluster instantiation (HPC workloads) (Fei et al., 2024, Laszewski et al., 30 Jul 2025).
• Provenance and Metadata Stores: File-based (MLXP, Clowdr), relational (SCHEMA lab, E2Clab), property-graph (ProvDB), or hybrid approaches store rich provenance, configuration, and performance metadata for every run or artifact (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Miao et al., 2016, Rosendo et al., 2021).
• Monitoring and Logging: Unified logging and metric aggregation per run/task (e.g., log directories per run, OML/OMSP time series, performance/energy metrics, error logs), enabling live and post-hoc introspection (Arbel et al., 2024, Vargas-Solar et al., 30 Sep 2025, Rakotoarivelo et al., 2014, Fei et al., 2024).

The system logic typically ensures:

• Isolation of outputs, strict mapping between run (or workflow execution) and config, and versioned code/artifact linkage (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Laszewski et al., 30 Jul 2025).
• Automated scheduling, dependency-aware job dispatch, and resource-reservation to guarantee concurrency safety in both digital and physical labs (Fei et al., 2024, Adamidi et al., 1 Apr 2025).

3. Provenance, Reproducibility, and Metadata Management

High-fidelity, reproducible experiment orchestration fundamentally relies on comprehensive capture and linkage of all forms of provenance:

• Configuration Capture: YAML/JSON/XML declarative configs and full CLI/overrides preserved per run, with resolved values merged and stored (MLXP, SCHEMA lab, LabWiki) (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Rakotoarivelo et al., 2014).
• Code/Environment Versioning: Code snapshots or git commit hashes at submission (MLXP, Experiments as Code), container image digests, environment variables and dependency manifests, and, in workflow-centric systems, explicit environment encapsulation for each task (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Aguilar et al., 2022).
• Artifact and Result Lineage: Integrated tracking of checkpoints, model artifacts, metrics, outputs, intermediate files, through file-system or database pointers, SHA digests, and directory trees (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Miao et al., 2016).
• Process and Data Provenance Graphs: Graph representations capturing versions, artifacts, derivations, and transformations enable traversable analyses (ProvDB property-graph, SCHEMA lab relational schema, Experiversum metadata repository) (Miao et al., 2016, Adamidi et al., 1 Apr 2025, Vargas-Solar et al., 30 Sep 2025).
• Action/Decision Metadata: Some systems include contextual meta-records of collaborative decisions, sign-offs, and pipeline role/rationale for full human-computer traceability (Experiversum, SCHEMA lab) (Vargas-Solar et al., 30 Sep 2025, Adamidi et al., 1 Apr 2025).

Reproducibility guarantees typically extend to deterministic recomputation (run with the same config/code yields the same result), cross-infrastructure replicability (workflow/container execution), and empirical quantification of run-to-run variability via aggregation and grouping interfaces (Arbel et al., 2024, Adamidi et al., 1 Apr 2025).

4. Integration with Scheduling, Pipelines, and External Tools

Modern orchestrated experiment frameworks are built for heterogeneity, extensibility, and interoperability:

• Scheduler/Cluster Adaptation: Native support for prevalent schedulers (SLURM, PBS, LSF, OAR, SGE, AWS Batch, Kubernetes TESK, high-throughput clusters) is standard, including parameter sweep expansion and parallel, distributed execution (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Laszewski et al., 30 Jul 2025, Kiar et al., 2018).
• Workflow Systems & Pipeline Managers: MLXP and similar frameworks are designed as drop-in decorators or API layers that seamlessly embed in larger managers (Airflow, Luigi, Snakemake, Nextflow), or are called as pipeline steps in broader data/analysis DAGs (Arbel et al., 2024, Adamidi et al., 1 Apr 2025).
• Containerization and Cloud-Native Environments: Container image specification is first-class (Docker, Singularity), and orchestration engines manage not only execution but artifact movement across hybrid local/HPC/cloud (Adamidi et al., 1 Apr 2025, Kiar et al., 2018, Volponi et al., 2024).
• Integration Points for HPO and Analysis: Direct support or extension to plug in hyperparameter optimization (Hydra sweeps, Ray Tune, Optuna), advanced statistical aggregation (mean, std, error bars), and parallel experimental campaigns (Arbel et al., 2024, Laszewski et al., 30 Jul 2025).
• Automated Teardown, Cleanup, and Reporting: Finalization steps, cleanup of resources, and automatic packaging/export of logs, results, and metadata for publication or persistent archiving (Vargas-Solar et al., 30 Sep 2025, Adamidi et al., 1 Apr 2025, Rakotoarivelo et al., 2014).

5. Case Studies, Performance, and Best Practices

Deployed frameworks consistently demonstrate impact via large-scale, reproducible multi-experiment campaigns across diverse scientific domains:

Framework	Domain/Use Case	Scale/System	Key Performance/Outcome
MLXP	ML algorithm comparisons	Up to 105 runs per batch, local/HPC	Deterministic code/config/SHA per run, mean±std aggregation for transparency (Arbel et al., 2024)
SCHEMA lab	Bioinformatics pipelines	Container DAG on Kubernetes	Provenance/quotas, per-task resource metrics, workflow grouping and export (Adamidi et al., 1 Apr 2025)
Experiversum	Social, Earth, and Life Sci	Lakehouse, 10⁶+ entries	Metadata queries <200ms, full pipelined lineage (Vargas-Solar et al., 30 Sep 2025)
AlabOS	Autonomous materials lab	3,500 samples, 28 devices	Real-time task scheduling, robust error recovery, <1% unrecoverable errors (Fei et al., 2024)
LabWiki	Networked experimentation	SFA/GENI/FIRE testbeds	Plan/Prepare/Execute/Analyze loop, OML streaming, GUI-driven lifecycle (Rakotoarivelo et al., 2014)
Cloudmesh EE/SmartSim	HPC/AI/ML benchmarking	30+ to thousands of jobs	Template-based gridsearch, federated/ensemble execution, cost tracing (Laszewski et al., 30 Jul 2025)

Best practices repeatedly emphasized:

• Explicit, versioned, and modular descriptors/configs, distinct from resource credentials.
• Fine-grained isolation per run, deterministic capture of inputs, outputs, and environmental context.
• Automation of critical-but-error-prone steps (checkpointing, multi-run sweeps, artifact collection).
• Support for both low-level APIs and high-level YAML (or GUI) for rapid on-ramp and reproducibility (Laszewski et al., 30 Jul 2025, Arbel et al., 2024, Rakotoarivelo et al., 2014, Aguilar et al., 2022).
• Integration with FAIR and open science standards for publication, archival, and cross-team sharing (Laszewski et al., 30 Jul 2025, Vargas-Solar et al., 30 Sep 2025).

6. Emerging Directions and Impact

Orchestrated experiment lifecycle management is converging on a set of domain-agnostic principles—workflow formalisms, provenance-rich metadata, containerization, and robust scheduler integration—that now underpin reproducibility and scalability across computational science, ML, edge/cloud analytics, and autonomous labs.

Recent work highlights:

• Broadening from ML/HPC/analytic workflows to automated laboratory environments (AlabOS), complex hardware/software coordination (TALOS), and edge-to-cloud distributed analytics (E2Clab) (Fei et al., 2024, Volponi et al., 2024, Rosendo et al., 2021).
• Deep integration with hybrid computational pipelines, supporting both exploratory and highly structured research paradigms via transparent lineage and meta-decision logging (Experiversum, ProvDB, SCHEMA lab) (Vargas-Solar et al., 30 Sep 2025, Miao et al., 2016, Adamidi et al., 1 Apr 2025).
• Increasing emphasis on automation of reuse (case-based reasoning), resource optimization, error analysis/handling, and collaborative decision capture (Cederbladh et al., 12 Sep 2025, Fei et al., 2024, Vargas-Solar et al., 30 Sep 2025).

By systematically orchestrating all phases and artifacts, these frameworks eliminate brittle, manual infrastructure and promote scientific transparency, accountability, and reproducibility at scale (Arbel et al., 2024, Adamidi et al., 1 Apr 2025, Laszewski et al., 30 Jul 2025, Vargas-Solar et al., 30 Sep 2025).